Deep Learning (TensorFlow, PyTorch) — CNNs, RNNs, and transformer architectures

Categories: AI/ML, CSE

About Course

The Deep Learning (TensorFlow, PyTorch) course provides a hands-on, project-driven pathway to mastering modern neural network architectures.
You’ll explore the full deep learning ecosystem — starting from artificial neural networks (ANNs) to CNNs for vision, RNNs for sequence modeling, and Transformer architectures for advanced AI tasks.

Using industry-leading frameworks TensorFlow and PyTorch, you’ll train, tune, and deploy models for real-world datasets.
By the end, you’ll be able to design and implement complex deep learning solutions — equipping you for roles like AI Engineer, Deep Learning Developer, or Research Associate.

Understand deep learning fundamentals and neural network architectures.
Build and train models using TensorFlow and PyTorch.
Implement feed-forward, convolutional, and recurrent neural networks.
Master optimization, backpropagation, and activation functions.
Explore CNNs for image classification and object recognition.
Develop RNNs, LSTMs, and GRUs for sequence and text data.
Implement Transformer and Attention-based models (BERT/GPT concepts).
Apply transfer learning, fine-tuning, and model deployment techniques.

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Deep Learning (TensorFlow, PyTorch) — CNNs, RNNs, and transformer architectures

About Course

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